Imaging Mars analog minerals' reflectance spectra and testing mineral detection algorithms

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

Download Full-text

Performance of Power-Line-Signaling Based Detection Algorithms for Islanding Protection of Distributed Generators in Interharmonic Polluted Systems

Renewable Energy and Power Quality Journal ◽

10.24084/repqj10.272 ◽

2012 ◽

pp. 202-207 ◽

Cited By ~ 1

Author(s):

H. Jouybari-Moghaddam ◽

G.B. Gharehpetian ◽

S.H. Hosseinian

Keyword(s):

Power Line ◽

Detection Algorithms ◽

Distributed Generators

Download Full-text

Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1634-07 ◽

1998 ◽

Vol 1634 (1) ◽

pp. 56-63 ◽

Cited By ~ 13

Author(s):

Sherif S. Ishak ◽

Haitham M. Al-Deek

Keyword(s):

Neural Networks ◽

False Alarm ◽

False Alarm Rate ◽

Back Propagation ◽

Incident Detection ◽

Traffic Patterns ◽

Traffic Pattern ◽

Detection Algorithms ◽

Fuzzy Art ◽

Freeway Incidents

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

Download Full-text